Medical image data can originate from the most disparate image-generating devices, referred to as modalities. Modalities of this type include for example computed tomography scanners, magnetic resonance tomography systems, simple x-ray machines for generating projection images, etc. Image data from various modalities may be available in relation to a patient. The image data is stored in a shared image processing system (PACS) within a clinical environment for example. For processing purposes the image data from said image storage system is displayed or loaded for image processing and image evaluation at the request of a particular local client computer. The corresponding image processing program (application) is normally installed on a powerful server. In this scenario different applications are available for image processing purposes and are selected accordingly as a function of the requirements.
The image data is typically stored in accordance with the so-called DICOM standard (DICOM=Digital Imaging and Communications in Medicine). The image data comprises META data, which conceptually represents an element called the “header”, and the actual image data containing the image information as pixel data.
As a result of the increasingly higher resolution of present-day digital image-generating devices the image data of an examined object is on occasion—depending on modality—very extensive, with a storage requirement of several gigabytes.
These large data volumes result in a deterioration in performance during the loading of the data, for example into a working memory of the server which the image processing program accesses in order to evaluate and analyze the image data. This leads in some cases to acceptance problems on the part of the medical personnel as users of applications of said type.
US 2009/0132636 A1 discloses a method in which so-called loading plans are provided for a delivery chain in order to deliver the data to different points, wherein for example in the event of multiple requests from different client computers the data is routed via suitable data node points taking into account data bottlenecks.